Novel robust minimum error entropy wasserstein distribution kalman filter under model uncertainty and non-gaussian noise

被引:5
作者
Feng, Zhenyu [1 ]
Wang, Gang [2 ]
Peng, Bei [1 ]
He, Jiacheng [1 ]
Zhang, Kun [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Robust Kalman filter; Wasserstein distribution; Model uncertainty; Non-Gaussian noise; Minimum error entropy; PARTICLE FILTERS;
D O I
10.1016/j.sigpro.2022.108806
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Recently, the divergence-based minimax approach was proposed and introduced to robust Kalman filtering under model uncertainty and Gaussian noise, and it significantly outperformed the standard Kalman filter. In this study, we consider a new Wasserstein-distribution based Kalman filter in which model uncertainty and non-Gaussian noise exist simultaneously. First, we construct a joint Gaussian distribution of states and observations in state space. Then, we set the equivalent estimator joint Gaussian distribution as a Wasserstein ambiguity set allowable neighborhood centered on the true distribution. Thus, the estimator distribution problem is transformed into a minimax problem, and the model uncertainty is tolerated accordingly. Subsequently, we introduce minimum error entropy to optimize the minimax problem based on Wasserstein ambiguity sets, so as to handle the influence of non-Gaussian noise. The minimax problem constrained by the minimum error entropy is transformed into a semi-positive definite convex optimization problem. By constructing two iterative sub-problems that are mutually conditional, the nonlinear semi-definite program finite convex optimization problem is solved. Finally, the minimum error entropy Wasserstein distribution Kalman filter algorithm is proposed. Additionally, the convergence of the proposed algorithm is clarified, and its effectiveness verified by comparing a series of algorithms in typical simulation scenarios. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:11
相关论文
共 33 条
[1]   A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].
Arulampalam, MS ;
Maskell, S ;
Gordon, N ;
Clapp, T .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) :174-188
[2]   Particle Learning and Smoothing [J].
Carvalho, Carlos M. ;
Johannes, Michael S. ;
Lopes, Hedibert F. ;
Polson, Nicholas G. .
STATISTICAL SCIENCE, 2010, 25 (01) :88-106
[3]   Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection [J].
Chang, Tsang-Kai ;
Chen, Kenny ;
Mehta, Ankur .
IEEE TRANSACTIONS ON ROBOTICS, 2022, 38 (01) :197-208
[4]   Minimum Error Entropy Kalman Filter [J].
Chen, Badong ;
Dang, Lujuan ;
Gu, Yuantao ;
Zheng, Nanning ;
Principe, Jose C. .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (09) :5819-5829
[5]   Maximum correntropy Kalman filter [J].
Chen, Badong ;
Liu, Xi ;
Zhao, Haiquan ;
Principe, Jose C. .
AUTOMATICA, 2017, 76 :70-77
[6]   Aggregated Wasserstein Distance and State Registration for Hidden Markov Models [J].
Chen, Yukun ;
Ye, Jianbo ;
Li, Jia .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42 (09) :2133-2147
[7]   Dual Extended Kalman Filter Under Minimum Error Entropy With Fiducial Points [J].
Dang, Lujuan ;
Chen, Badong ;
Xia, Yili ;
Lan, Jian ;
Liu, Meiqin .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (12) :7588-7599
[8]   Robust Power System State Estimation With Minimum Error Entropy Unscented Kalman Filter [J].
Dang, Lujuan ;
Chen, Badong ;
Wang, Shiyuan ;
Ma, Wentao ;
Ren, Pengju .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (11) :8797-8808
[9]   A competitive minimax approach to robust estimation of random parameters [J].
Eldar, YC ;
Merhav, N .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2004, 52 (07) :1931-1946
[10]   Innovative and Additive Outlier Robust Kalman Filtering With a Robust Particle Filter [J].
Fisch, Alexander T. M. ;
Eckley, Idris A. ;
Fearnhead, Paul .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2022, 70 :47-56